图 书 馆
生成LDA主题模型对主题意义排名Topic Significance Ranking of LDA Generative Models |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_alsumait_tsrlda/ |
| 主讲教师: | Loulwah AlSumait |
| 开课单位: | 科威特大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant words, or represent insignificant themes. Current approaches to topic modeling perform manual examination to find meaningful topics. This paper presents the first automated unsupervised analysis of LDA models to identify junk topics from legitimate ones, and to rank the topic significance. Basically, the distance between a topic distribution and three definitions of “junk distributions” is computed using a variety of measures, from which an expressive figure of the topic significance is implemented using 4-phase Weighted Combination approach. Our experiments on synthetic and benchmark datasets show the effectiveness of the proposed approach in ranking the topic significance. |
| 关 键 词: | 自动生成文本; 自动无监督; 语料库 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-23：lxf |
| 阅读次数: | 120 |